• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.37-49
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• 2001

We propose an optimization of mutual information for multiresolution image registration to represent useful information as integrated form obtaining from complementary information of multi modality images. The method applies mutual information as cost function to measure the statistical dependency or information redundancy between the image intensities of corresponding pixels in both images, which is assumed to be maximal if the images are geometrically aligned. As experimental results we validate visual inspection for accuracy, changning initial condition and addictive noise for robustness. Since our method uses the native image rather than prior feature extraction, few user interaction is required to perform the registration. In addition it leads to robust density estimation and convergence as applying non-parametric density estimation and stochastic multiresolution optimization.

• Journal of the Korean Solar Energy Society
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• v.34 no.4
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• pp.23-29
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• 2014

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.6
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• pp.403-410
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• 2009

This paper proposes a new approach to find the optimum ratios between sizes of the heat exchangers of the heat recovery steam generator (HRSG) system with limited size to maximize the efficiency of the steam turbine (bottom) cycle of combined cycle power plants (CCPP), but without performing the bottom cycle analysis. This could be achieved by minimizing the unavailable exergy (the sum of the destroyed and the lost exergies) resulted from the heat transfer process of the HRSG system. The present approach is relatively simple and straightforward because the process of the trial-and-error method, typical in performing the bottom cycle analysis for the system optimization, could be avoided. To demonstrate the usefulness of the present method, a single-stage HRSG system was chosen and the optimum evaporation temperature was obtained corresponding to the condition of the maximum useful work. The results show that the optimum evaporation temperature based on the present exergy analysis appears similar to that based on the bottom cycle analysis. Also shown is the dependency of size (NTU) ratios between the heat exchangers on the inlet gas temperature, which is another important factor in determining the optimum condition once overall size of the heat recovery steam generator is given. The present approach turned out to be a useful tool for optimization of the singlestage HRSG systems and can easily be extended to multi-stage systems.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.279-287
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• 2010

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.2
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• pp.23-38
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• 1990

In this study, decompositive optimization method of two levels was selected to optimize effectively the geometry of the truss which takes the multi-loading condition, and the allowable stress, bucking stress, displacement and natural frequency constraints into consideration. The algorithm of this study is made up of sectional optimization using the feasible direction method in level 1, and geometrical optimization employing Powell's one-direction search method which menimizes only objictive function in level 2. The results of this study acquired by beenning applied to structural model of the truss are as follows : 1. It is verified that the algorithm of this study effectively converges, independent of the initial geometry of the truss and the applied various constraints. 2. The optimum goemetry of the truss varies more considerably according to the constraints selected. 3. Under the condition of the same design, the weight of the truss can be decreased more considerably by means of optimizing even the geometry of truss than by means of optimizing the section of truss while fixing geometrical configuration of it, even though there might be a little difference according to the initial geometry of the truss and the design condition.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.89-102
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• 2010

Future uncertainty on water demand caused by future climate condition and water consumption leads a difficulty to determine the reservoir operation rule for supplying sufficient water to users. It is, thus, important to operate reservoirs not only for distributing enough water to users using the limited water resources but also for preventing floods and drought under the unknown future condition. In this study, the reservoir storage is determined in the first stage when future condition is unknown, and then, water distribution to users and river stream is optimized using the available water resources from the first stage decision using 2-stage stochastic linear programming (2-SLP). The objective function is to minimize the difference between target and actual water storage in reservoirs and the water shortage in users and river stream. Hedging rule defined by a precaution against severe drought by restricting outflow when reservoir storage decreases below a target, is also applied in the reservoir operation rule for improving the model applicability to the real system. The developed model is applied in a system with five reservoirs in the Han River basin, Korea to optimize the multi-reservoir system under various future water demand scenarios. Three multi-purposed dams - Chungju, Hoengseong, and Soyanggang - are considered in the model. Gwangdong and Hwacheon dams are also considered in the system due to the large capacity of the reservoirs, but they are primarily for water supply and power generation, respectively. As a result, the water demand of users and river stream are satisfied in most cases. The reservoirs are operated successfully to store enough water during the wet season for preparing the coming drought and also for reducing downstream flood risk. The developed model can provide an effective guideline of multi-reservoir operation rules in the basin.

• The Journal of Korean Society for Radiation Therapy
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• v.18 no.2
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• pp.119-125
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• 2006

Purpose: To measure the dose for dose optimization at the reference point (A, B) and the critical organ with multi Purpose brachytherapy phantom (MPBP). For this wort the MPBP was custom made, and designed to reconstruct the treatment applicator using multi function applicator (MFA) in the same way as the treatment of patient. Materials and Methods: Dose measurements were made at the reference points (A, B) and the bladder with thermoluminescence dosimeter (TLD) for four patients with tandem and ovoid of uterine cervix cancer using the phantom. In Phantom, Total 20 times of the measurements were made with 5 times a patient. Results: The results of TLD measurements in MPBP phantom showed the relative error ranging from -3.2% to 3.8% at A point, and -1.4% to 4% at B point and 1.3% to 7.15% at the bladder of reference point. Conclusion: The reproducibility of dose measurement under the same condition as the treatment could be achieved using the custom-made MFA in phantom and the dose at the reference point (A, B) and bladder could be analyzed accurately. The measured dose acquired in MPBP can apply for the dose optimization.

• Journal of Korean Society of Steel Construction
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• v.16 no.1 s.68
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• pp.123-134
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• 2004

In this research, a multilevel decomposition technique to enhance the efficiency of the configuration optimization of truss structures was proposed. On the first level, the nonlinear programming problem was formulated considering cross-sectional areas as design variables, weight, or volume as objective function and behavior under multiloading condition as design constraint. Said nonlinear programming problem was transformed into a sequential linear programming problem. which was effective in calculation through the approximation of member forces using behavior space approach. Such approach has proven to be efficient in sensitivity analysis and different form existing shape optimization studies. The modified method of feasible direction (MMFD) was used for the optimization process. On the second level, by treating only shape design variables, the optimum problem was transformed into and unconstrained optimal design problem. A unidirectional search technique was used. As numerical examples, some truss structures were applied to illustrate the applicability. and validity of the formulated algorithm.

• Proceedings of the Korean Geotechical Society Conference
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• 1998.05a
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• pp.3-34
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• 1998

The stability condition of rock slopes is greatly affected by the geometry and strength parameters of discontinuities in the rock masses. Rock slopes Involving movement of rock blocks on discontinuities are failed by one or combination of the three basic failure modes-plane, wedge, and toppling. In rock mechanics, practically all the parameters such as the joint set characteristics, the rock strength properties, and the loading conditions are always subject to a degree of uncertainty. Therefore, a reasonable assessment of the rock slope stability has to include the excavation of the multi-failure modes, the consideration of uncertainties of discontinuity characteristics, and the decision on stabilization measures with favorable cost conditions. This study was performed to provide a new numerical model of the deterministic analysis, reliability analysis, and reliability-based optimization for rock slope stability. The sensitivity analysis was carried out to verify proposed method and developed program; the parameters needed for sensitivity analysis are design variables, the variability of discontinuity properties (orientation and strength of discontinuities), the loading conditions, and rock slope geometry properties. The design variables to be optimized by the reliability-based optimization include the cutting angle, the support pressure, and the slope direction. The variability in orientations and friction angle of discontinuities, which can not be considered in the deterministic analysis, has a greatly influenced on the rock slope stability. The stability of rock slopes considering three basic failure modes is more influenced by the selection of slope direction than any other design variables. When either plane or wedge failure is dominant, the support system is more useful than the excavation as a stabilization method. However, the excavation method is more suitable when toppling failure is dominant. The case study shows that the developed reliability-based optimization model can reasonably assess the stability of rock slopes and reduce the construction cost.